Probabilistic Weather Forecasting in R

Authors

Chris Fraley, University of Washington
Adrian Raftery, University of Washington
Tilmann Gneiting, Heidelberg University
McLean Sloughter, Seattle University
Veronica Berrocol, University of Michigan

Date of this Version

6-2011

Document Type

Article

Citation

The R Journal (June 2011) 3(1)

Comments

Copyright 2011, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

This article describes two R packages for probabilistic weather forecasting, ensembleBMA, which offers ensemble post-processing via Bayesian model averaging (BMA), and Prob ForecastGOP, which implements the geostatistical output perturbation (GOP) method. BMA forecasting models use mixture distributions, in which each component corresponds to an ensemble member, and the form of the component distribution depends on the weather parameter (temperature, quantitative precipitation or wind speed). The model parameters are estimated from training data. The GOP technique uses geostatistical methods to produce probabilistic fore casts of entire weather fields for temperature or pressure, based on a single numerical forecast on a spatial grid. Both packages include functions for evaluating predictive performance, in addition to model fitting and forecasting.